WordNet BETA release 2

WordNet

Create a Simple network of words related to each other using Twitter Streaming API.

Major parts of this project.

• Streamer : ~/twitter_streaming.py
• TF-IDF Gene : ~/wordnet/tf_idf_generator.py
• NN words Gene :~/ wordnet/nn_words.py
• NETWORK Gene : ~/wordnet/word_net.py

Using Streamer Functionality

1. Unzip the Source and run on bash '$pip install -r requirements.txt' @ root directory and you will be ready to go..

2. Go to root-dir(~), Create a config.py file with details mentioned below:

   # Variables that contains the user credentials to access Twitter Streaming API
   # this link will help you(http://socialmedia-class.org/twittertutorial.html)
   access_token = "xxx-xx-xxxx"
   access_token_secret = "xxxxx"
   consumer_key = "xxxxxx"
   consumer_secret = "xxxxxxxx"

3. run Streamer with an array of filter words that you want to fetch tweets on. eg. $python twitter_streaming.py hello hi hallo namaste > data_file.txt this will save a line by line words from tweets filtered according to words used as args in data_file.txt.

Using WordNet Module

1. Unzip the Source and install wordnet module using this script,

    $python setup.py install
   

2. To create a TF-IDF structure file for every doc, use:

   from wordnet import find_tf_idf
   
   df, tf_idf = find_tf_idf(
   file_names=['file/path1','file/path2',..],       # paths of files to be processed.(create using twitter_streamer.py)
   prev_file_path='prev/tf/idf/file/path.tfidfpkl', # prev TF_IDF file to modify over, format standard is .tfidfpkl. default = None
   dump_path='path/to/dump/file.tfidfpkl'           # dump_path if tf-idf needs to be dumped, format standard is .tfidfpkl. default = None
   )
   
   '''
   if no file is provided prev_file_path parameter, new TF-IDF file will be generated ,and else
   TF-IDF values will be combined with previous file, and dumped at dump_path if mentioned,
   else will only return the new tf-idf list of dictionaries, and df dictionary.
   '''

3. To use NN Word Gene of this module, simply use wordnet.find_knn:

   from wordnet import find_knn
   
   words = find_knn(
   tf_idf=tf_idf,       # this tf_idf is returned by find_tf_idf() above.
   input_word='german', # a word for which k nearest neighbours are required.
   k=10,                # k = number of neighbours required, default=10
   rand_on=True         # rand_on = either to randomly skip few words or show initial k words default=True
   )
   
   '''
   This function will return a list of words closely related to provided input_word refering to
   tf_idf var provided to it. either use find_tf_idf() to gather this var or pickle.load() a dump
   file dumped by the same function at your choosen directory. the file contains 2 lists in format
   (idf, tf_idf).
   '''

4. To create a Word Network, use :

   from wordnet import generate_net
   
   word_net = generate_net(
   df=df,                          # this df is returned by find_tf_idf() above.
   tf_idf=tf_idf,                  # this tf_idf is returned by find_tf_idf() above.
   dump_path='path/to/dump.wrnt'   # dump_path = path to dump the generated files, format standard is .wrnt. default=None
   )
   
   '''
   this function returns a list of Word entities.
   '''

5. To retrieve a Word Network, use :

   from wordnet import retrieve_net
   
   word_net = retrieve_net(
       'path/to/network.wrnt' # path to network file, format standard is .wrnt.
       )
   '''
   this function returns a list of Word entities.
   '''

Test Run

To run a formal test, simply run this script. python test.py, this module will return 0 if everythinig worked as expected.

test.py uses sample data provided here and executes unittest on find_tf_idf(), find_knn() & generate_net().

by @Anurag